Environmental governance is an important component of the national governance system. China’s current environmental problems are particularly complex. How to let the government, enterprises, and the public participate in environmental governance is the key to enhance the ability of environmental governance. Based on the evolutionary game theory, the interaction and influencing factors among enterprise pollution control, government supervision, and public participation are analyzed, and the empirical analysis is carried out based on China’s 30 provincial panel data from 2009 to 2018. The research results show that government supervision has a positive effect on the environmental governance and can urge enterprises to actively perform pollution control. The effect of government supervision is constrained by the income and cost of enterprises, and the penalties for passive pollution control should be raised. At the same time, improving the government’s reputation loss can effectively stimulate the government’s environmental supervision behavior. Public participation significantly promotes the governance effect of three industrial wastes, and the enthusiasm of public participation is closely related to participation cost and psychological benefits. Public participation can replace government supervision to a certain extent. The interaction between government and public has a positive effect on environmental governance. The research results will help to build an effective environmental governance system and improve environmental governance performance and public satisfaction.

The uncertainty and complexity of natural hazards put forward new requirements for emergency management systems. In order to deal with natural hazards effectively, it is important to build a cooperative network between government organizations and social organizations. The social network analysis method is adopted, the April 2013 Ya’an China earthquake is taken as a case study, the institutionalized emergency organization network before the disaster and the actual response organization network after the disaster are analyzed, and centrality, between centrality, closeness centrality and core-periphery are calculated. Through qualitative and quantitative research, the functions of social organization in the process of natural hazards emergency relief are revealed, the role orientation of social organization in the emergency management network is analyzed, and the influence factors of the social organization participation in the natural hazards relief is pointed out. Research results will help to promote the cooperation between social organization and government, and improve the efficiency of natural hazards emergency relief.

Data-driven technologies and automated identification systems (AISs) provide unprecedented opportunities for maritime surveillance. As part of enhancing maritime situational awareness and safety, in this paper, we address the issue of predicting a ship’s future trajectory using historical AIS observations. The objective is to use past data in the training phase to learn the predictive distribution of marine traffic patterns and then use that information to forecast future trajectories. To achieve this, we investigate an encoder–decoder architecture-based sequence-to-sequence prediction model and CNN model. This architecture includes a long short-term memory (LSTM) RNN that encodes sequential AIS data from the past and generates future trajectory samples. The effectiveness of sequence-to-sequence neural networks (RNNs) for forecasting future vessel trajectories is demonstrated through an experimental assessment using an AIS dataset.

Whole-genome alignment (WGA) is a critical process in comparative genomics, facilitating the detection of genetic variants and aiding our understanding of evolution. This paper offers a detailed overview and categorization of WGA techniques, encompassing suffix tree-based, hash-based, anchor-based, and graph-based methods. It elaborates on the algorithmic properties of these tools, focusing on performance and methodological aspects. This paper underscores the latest progress in WGA, emphasizing the increasing capacity to manage the growing intricacy and volume of genomic data. However, the field still grapples with computational and biological hurdles affecting the precision and speed of WGA. We explore these challenges and potential future solutions. This paper aims to provide a comprehensive resource for researchers, deepening our understanding of WGA tools and their applications, constraints, and prospects.

Purpose: Although classical techniques for image segmentation may work well for some images, they may perform poorly or not work at all for others. It often depends on the properties of the particular image segmentation task under study. The reliable segmentation of brain tumors in medical images represents a particularly challenging and essential task. For example, some brain tumors may exhibit complex so-called “bottle-neck” shapes which are essentially circles with long indistinct tapering tails, known as a “dual tail.” Such challenging conditions may not be readily segmented, particularly in the extended tail region or around the so-called “bottle-neck” area. In those cases, existing image segmentation techniques often fail to work well.Methods: Existing research on image segmentation using wormhole and entangle theory is first analyzed. Next, a random positioning search method that uses a quantum-behaved particle swarm optimization (QPSO) approach is improved by using a hyperbolic wormhole path measure for seeding and linking particles. Finally, our novel quantum and wormhole-behaved particle swarm optimization (QWPSO) is proposed.Results: Experimental results show that our QWPSO algorithm can better cluster complex “dual tail” regions into groupings with greater adaptability than conventional QPSO. Experimental work also improves operational efficiency and segmentation accuracy compared with current competing reference methods.Conclusion: Our QWPSO method appears extremely promising for isolating smeared/indistinct regions of complex shape typical of medical image segmentation tasks. The technique is especially advantageous for segmentation in the so-called “bottle-neck” and “dual tail”-shaped regions appearing in brain tumor images.